Like voice activity detection, clustering is one of the most important part of any speaker diarization pipeline.
It consists in grouping speech segments according to the actual identity of the speaker. As of October 2019, most best performing speaker diarization systems rely on x-vectors as input to the clustering step. They are usually extracted from a fixed-length sliding window, and the pairwise similarity matrix is obtained with probabilistic linear discriminant analysis (PLDA).
There exist plenty of open-source implementations of x-vectors and PLDA already [1]}, [2]}, and pyannote.audio does not provide yet another version of those approaches.
